Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting

Chiun-Sin Lin, Sheng-Hsiung Chiu and Tzu-Yu Lin

Economic Modelling, 2012, vol. 29, issue 6, 2583-2590

Abstract: To address the nonlinear and non-stationary characteristics of financial time series such as foreign exchange rates, this study proposes a hybrid forecasting model using empirical mode decomposition (EMD) and least squares support vector regression (LSSVR) for foreign exchange rate forecasting. EMD is used to decompose the dynamics of foreign exchange rate into several intrinsic mode function (IMF) components and one residual component. LSSVR is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for foreign exchange rates. Empirical results show that the proposed EMD-LSSVR model outperforms the EMD-ARIMA (autoregressive integrated moving average) as well as the LSSVR and ARIMA models without time series decomposition.

Keywords: Empirical mode decomposition; Least-squares support vector regression; Foreign exchange rate forecasting; Intrinsic mode function (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:29:y:2012:i:6:p:2583-2590

DOI: 10.1016/j.econmod.2012.07.018

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